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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"
11889 条 记 录,以下是181-190 订阅
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SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining
SyncMask: Synchronized Attentional Masking for Fashion-centr...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea
vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conv... 详细信息
来源: 评论
USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation
USE: Universal Segment Embeddings for Open-Vocabulary Image ...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Wang, Xiaoqi He, Wenbin Xuan, Xiwei Sebastian, Clint Ono, Jorge Piazentin Li, Xin Behpour, Sima Thang Doan Gou, Liang Shen, Han-Wei Ren, Liu Bosch Res North Amer Oak Brook Terrace IL 60181 USA Bosch Ctr Artificial Intelligence Baden Baden Germany Ohio State Univ Columbus OH 43210 USA Univ Calif Davis Davis CA 95616 USA
The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such... 详细信息
来源: 评论
Beyond Image Super-Resolution for Image recognition with Task-Driven Perceptual Loss
Beyond Image Super-Resolution for Image Recognition with Tas...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Kim, Jaeha Oh, Junghun Lee, Kyoung Mu Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea
In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image su... 详细信息
来源: 评论
Efficient Test-Time Adaptation of vision-Language Models
Efficient Test-Time Adaptation of Vision-Language Models
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Karmanov, Adilbek Guan, Dayan Lu, Shijian El Saddik, Abdulmotaleb Xing, Eric Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Nanyang Technol Univ Singapore Singapore Univ Ottawa Ottawa ON Canada Carnegie Mellon Univ Pittsburgh PA 15213 USA
Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance, the...
来源: 评论
StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
StableVITON: Learning Semantic Correspondence with Latent Di...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea
Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim... 详细信息
来源: 评论
Flexible Biometrics recognition: Bridging the Multimodality Gap through Attention, Alignment and Prompt Tuning
Flexible Biometrics Recognition: Bridging the Multimodality ...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Tiong, Leslie Ching Ow Sigmund, Dick Chan, Chen-Hui Teoh, Andrew Beng Jin Samsung Elect Suwon South Korea AIDOT Inc Seoul South Korea Korea Inst Sci & Technol Seoul South Korea Yonsei Univ Seoul South Korea
Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we intr... 详细信息
来源: 评论
Sequential Modeling Enables Scalable Learning for Large vision Models
Sequential Modeling Enables Scalable Learning for Large Visi...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA
We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in wh... 详细信息
来源: 评论
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
PELA: Learning Parameter-Efficient Models with Low-Rank Appr...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Guo, Yangyang Wang, Guangzhi Kankanhalli, Mohan Natl Univ Singapore Singapore Singapore
Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Re-cent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to th... 详细信息
来源: 评论
Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems
Evaluating the Integration of Morph Attack Detection in Auto...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Panzino, Andrea la Cava, Simone Maurizio Orru, Giulia Marcialis, Gian Luca Univ Cagliari Piazza Armi I-09123 Cagliari Italy
Due to the possibility of automatically verifying an individual's identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with ... 详细信息
来源: 评论
Finding Lottery Tickets in vision Models via Data-driven Spectral Foresight Pruning
Finding Lottery Tickets in Vision Models via Data-driven Spe...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Iurada, Leonardo Ciccone, Marco Tommasi, Tatiana Politecn Torino Turin Italy
Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new prunin... 详细信息
来源: 评论